agents/analytics_pipeline.py

from google.adk.agents import LlmAgent
from tools import csv_parser, plot_generator, forecaster

trend_detector_agent = LlmAgent(
    name="trend_detector_agent",
    model="gemini-2.5-pro-exp-03-25",
    description="Detects trends and anomalies in business data.",
    instruction="""
        Analyze the input table. Identify major trends, seasonal patterns,
        and anomalies (spikes or drops). Return a concise summary.
    """,
    tools=[csv_parser.parse_csv_tool, plot_generator.plot_sales_tool]
)

forecast_agent = LlmAgent(
    name="forecast_agent",
    model="gemini-2.5-pro-exp-03-25",
    description="Forecasts future metrics from time series data.",
    instruction="""
        Forecast next 3 months of sales based on historical patterns.
        Use the forecast tool to generate a visual chart.
    """,
    tools=[forecaster.forecast_tool]
)

strategy_agent = LlmAgent(
    name="strategy_agent",
    model="gemini-2.5-pro-exp-03-25",
    description="Recommends strategic business decisions.",
    instruction="""
        Based on trends and forecasts, suggest optimization strategies
        across marketing, operations, and finance (ROI, CAC, churn).
    """
)

analytics_coordinator = LlmAgent(
    name="analytics_coordinator",
    model="gemini-2.5-pro-exp-03-25",
    description="Coordinates full BI pipeline: trends, forecast, strategy.",
    instruction="""
        Run the following:
        1. Analyze the CSV with trend_detector_agent
        2. Forecast future metrics using forecast_agent
        3. Recommend business strategies using strategy_agent
        Return a full dashboard-style summary.
    """,
    sub_agents=[trend_detector_agent, forecast_agent, strategy_agent]
)